2025 Spring Semester

Title: Enabling Ahead Prediction with Practical Energy Constraints

Speaker: Lingzhe Chester Cai, PhD Student, UT ECE

Date: Tuesday April 15th, 2025, 3:30pm

Location: EER 1.518 or Zoom Link

Abstract:

Decades of research on branch prediction results in complex prediction algorithms and large look up tables,  leading to a multi-cycle prediction latency, adversely impacting performance. Ahead prediction is a proposed solution to the predictor latency problem, but drastically increases prediction energy as exponentially more entries are read out for each branch skipped, making building such a predictor impractical. In this talk, I will show that only a few missing history patterns are observed in the program’s runtime. Using this insight, we present a new approach for building ahead predictors that does not require reading exponentially more entries for large ahead distances. Our ahead predictor provides a 4.4% performance improvement while increasing power by only 1.5x, as opposed to prior designs that incur a 14.6x energy overhead. By hiding the predictor latency from the rest of the pipeline, our work allows for larger and more complex predictors and better pipelining width scaling. In addition, our work implies that the direction of an easy-to-predict branch does not need to be pushed to the history, presenting opportunities for future branch predictor design.

Bio:

Chester Cai is a 7th year PhD student studying CPU microarchitecture under Professor Yale Patt. His research focuses on the CPU frontend, specifically branch prediction, balancing predictor accuracy, latency and throughput. Before Joining UT Austin, he obained his bachelor degree in Computer Engineering from Rose-Hulman Institute of Technology.

Title: RESCQ: Realtime Scheduling for Continuous Angle Quantum Error Correction Architectures

Speaker: Sayam Sethi, PhD Student, UT ECE

Date: Tuesday April 8th, 2025, 3:30pm

Location: EER 1.518 or Zoom Link

Abstract:

In order to realize large scale quantum error correction (QEC), resource states, such as |T〉, must be prepared which is expensive in both space and time. In order to circumvent this problem, alternatives have been proposed, such as the production of continuous angle rotation states. However, the production of these states is non-deterministic and may require multiple repetitions to succeed. The original proposals suggest architectures which do not account for realtime (or dynamic) management of resources to minimize total execution time. Without a realtime scheduler, a statically generated schedule will be unnecessarily expensive. We propose RESCQ (pronounced rescue), a realtime scheduler for programs compiled onto these continuous angle systems. Our scheme actively minimizes total cycle count by on-demand redistribution of resources based on expected production rates. Depending on the underlying hardware, this can cause excessive classical control overhead. We further address this by dynamically selecting the frequency of our recomputation. RESCQ improves over baseline proposals by an average of 2x in cycle count.

Bio:

Sayam Sethi is a PhD student in the ECE Department at The University of Texas at Austin, advised by Dr. Jonathan Baker. He is currently interested in architectural design for realising Fault-Tolerant Quantum Computers (FTQC), with a specific focus on scheduling realtime operations, and minimizing program runtime. Before joining UT, he obtained his B. Tech. in Computer Science and Engineering from IIT Delhi.

UT Austin Computer Architecture Seminar Series 2025 Spring

Sponsored by:

Date	Series	Topic	Speaker
January 24, 2025	Series 01	Securing Computer Systems using AI Methods and for AI Applications	Mulong Luo
April 8, 2025	Series 02	RESCQ: Realtime Scheduling for Continuous Angle Quantum Error Correction Architectures	Sayam Sethi
April 15, 2025	Series 03	Enabling Ahead Prediction with Practical Energy Constraints	Lingzhe Chester Cai

Title: Securing Computer Systems using AI Methods and for AI Applications

Speaker: Mulong Luo, Postdoctoral Researcher, UT ECE

Date: Friday January 24, 2025, 3:30pm

Location: EER 0.806/0.808 or Zoom Link

Abstract:

Securing modern computer systems against an ever-evolving threat landscape is a significant challenge that requires innovative approaches. Recent developments in artificial intelligence (AI), such as large language models (LLMs) and reinforcement learning (RL), have achieved unprecedented success in everyday applications. However, AI serves as a double-edged sword for computer systems security. On one hand, the superhuman capabilities of AI enable the exploration and detection of vulnerabilities without the need for human experts. On the other hand, specialized systems required to implement new AI applications introduce novel security vulnerabilities.

In this talk, I will first present my work on applying AI methods to system security. Specifically, I leverage reinforcement learning to explore microarchitecture attacks in modern processors. Additionally, I will discuss the use of multi-agent reinforcement learning to improve the accuracy of detectors against adaptive attackers. Next, I will highlight my research on the security of AI systems, focusing on retrieval-augmented generation (RAG)-based LLMs and autonomous vehicles. For RAG-based LLMs, my ConfusedPilot work demonstrates how an attacker can compromise confidentiality and integrity guarantees by sharing a maliciously crafted document. For autonomous vehicles, I reveal a software-based cache side-channel attack capable of leaking the physical location of a vehicle without detection. Finally, I will outline future directions for building secure systems using AI methods and ensuring the security of AI systems.

Bio:

Mulong Luo is currently a postdoctoral researcher at the University of Texas at Austin hosted by Mohit Tiwari. His research interests lie broadly in applying AI methods for computer architecture and system security, as well as improving the security of AI systems including LLM and autonomous vehicles. He is selected as a CPS Rising Star 2023. His paper is selected as a finalist in Top Picks in Hardware and Embedded Security 2022. He is also awarded the best paper award at CPS-SPC 2018. Mulong received Ph.D. at Cornell University advised by Edward Suh in 2023. He got his MS and BS from UCSD and Peking University respectively.